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Paper   IPM / Biological Sciences / 15029
School of Biological Sciences
  Title:   Nonparametric estimation of the entropy using a ranked set sample
1.  Morteza Amini
2.  Mahdi Mahdizadeh
  Status:   Published
  Journal: Communications in Statistics - Simulation and Computation
  Year:  2017
  Pages:   1-19
  Supported by:  IPM
This article is concerned with nonparametric estimation of the entropy in ranked set sampling. Theoretical properties of the proposed estimator are studied. The proposed estimator is compared with the rival estima- tor in simple random sampling. The applications of the proposed esti- mator to themutual information estimation as well as estimation of the Kullback–Leibler divergence are provided. Several Monté-Carlo simula- tion studies are conducted to examine the performance of the estima- tor. The results are applied to the longleaf pine (Pinus palustris) trees and the body fat percentage datasets to illustrate applicability of theoretical results.

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